This repository contains a streamlit application that allows a user to chat with their PDF documents. The user can ask a question about the document's contents in natural language, and the application will provide a response based on the content. A language model is used to generate answers.
The application follows these steps to provide responses to your questions:
- Text Extraction: Extracts text from the uploaded PDF documents.
- Text Chunking: The extracted text is divided into manageable chunks.
- LLM: The app uses a language model to generate vector representations - embeddings of the text chunks.
- Semantic Matching: When the user asks a question, the app compares it with the text chunks and identifies the semantically similar chunks.
- Response: The similar chunks are passed to the language model, which generates a response based on the content of the PDFs.
The model used in this project is google's flan-t5-xxl
- langchain
- PyPDF2
- python-dotenv
- streamlit
- faiss-cpu
- altair
- tiktoken
- huggingface-hub
- InstructorEmbedding
- sentence-transformers
- torch
- torchvision
- setuptools
pip install langchain PyPDF2 python-dotenv streamlit faiss-cpu altair tiktoken huggingface-hub InstructorEmbedding sentence-transformers torch torchvision setuptoolsObtain an API Token from HuggingFace and add it to the .env file in your directory
HUGGINGFACEHUB_API_TOKEN = your_api_key- Install the required dependencies.
- Run
app.pyto start the streamlit application, using
streamlit run app.py
- Upload your PDF documents using the file uploader.
- Click the Process button to process the uploaded documents.
- Ask questions through the chat.
This project uses Instructor to generate text embeddings and runs locally on your machine. The processing time will vary based on your hardware.